Tuesday, August 23, 2016

Advice to computational postdocs: apply to math and CS jobs

If you are a computational neuroscientist, and would like to teach, consider marketing yourself not only to neuro and psych departments, but to math and computer science as well.

Why? Because I'm looking at our place, and how we totally struggle to get good candidates in both computer science and applied math. I guess the cynical way to put it is that both fields are so incredibly useful these days that any person who is skillful in them, and who can also teach (which implies good management and interpersonal skills), can probably find jobs in the industry with much higher salaries. And with similar levels of enjoyment. Either way, the fact seems to be that applied math and computer science are understaffed, despite the high demand from the students. During job searches, for each decent job application we get in computer science, we get 10 applications in psychology, even when the research topics are actually quite comparable.

In practice it means that a good postdoc or grad student in computational neuroscience can at least triple their chances of landing a great TT job if they create two more sets of application documents: one tailored for applied math jobs, and another - for computer science. And while it may seem scary, it's actually pretty easy to do.

Let's give it a close look. In a SLAC, faculty typically teach 4 types of courses:
  1. Intro courses (something every major needs to take in lower college)
  2. Core courses (something every major needs to take in upper college)
  3. Fancy stuff (electives of various kinds)
  4. Crazy fun (like math for lit majors, or computer science for historians)
Basically, if you apply to math or CS dept as a neuroscientist, you need to make them know that you can teach all types of courses from this list, plus establish some "street credibility", so to say. Type (1) is never a problem: it would be "calculus I, II" in math (every computational person can do it), or intro to object-oriented programming in CS. You can do it. Type (3) is also easy: it would be what you do for a living, as a researcher, or maybe some one-two fields nearby; something like modeling, numerical computation, big data analysis, dynamical systems, machine learning, methods in Bayesian statistics, or something like that.

Which means that basically you just need to invent one crazy fun course (which should be relatively easy; just draw inspiration from your hobbies and side interests), and to convince the committee that you can teach core courses: something like linear algebra, differential equations or vector calculus in math; or data structures, algorithms, and discrete math in CS. That is a bit harder, but once you cover some of these courses (one may be enough), you are fine!

Now just reword your research statements accordingly, to compensate for the relative lack of "appropriate" education in these fields, and you are golden. You can apply to 3 times more positions than a straight neuro person would apply, and you would compete in a market with a much higher demand and lower supply, boosting your success rates.

Monday, August 22, 2016

Best way to create custom visualization pallets

Colorbrewer is awesome, but quite restrictive. After browsing the web for some time, here's the best too I found, with tools to create very nice-looking, yet usable and informative custom color scales in any aesthetics you want. It's called the "chroma scale helper":
http://gka.github.io/palettes/#colors=lightyellow,gray,teal,indigo|steps=5|bez=1|coL=1

Here's the description of how it works (it's very clever, and worth the read on its own, even if you never use the actual scale helper"
https://vis4.net/blog/posts/mastering-multi-hued-color-scales/

Here's a table of color names it uses (you may have to browse for the color you like, but it's very doable)
http://cng.seas.rochester.edu/CNG/docs/x11color.html

And finally, the source of these links (with some more advice on the matter of colors):
http://lisacharlotterost.github.io/2016/04/22/Colors-for-DataVis/

Wednesday, June 29, 2016

Teaching scientific critique

A very nice text on teaching how to critique scientific literature:

Main idea of the text: too many teaching assignments we use essentially encourage students to "bullshit"; to generate some plausible-looking, but empty rambling about the topic, or post-hoc interpretations of their results. It's hard to grade, it does not teach students real scientific thinking, it's just generally bad. The author then gives some good pieces of advice about how not to fall into this trap:
  1. Be more specific: offer a critique yourself, evaluate the paper, and, potentially, vindicate it. Send a clear message that our goal is not to find a flaw, but to be able to asses whether there's a flaw in the study. 
  2. Clearly separate critique of methods from critique of results. I fully agree here; students tend to conflate hypothesis-building, experiment design, and results interpretation; they somehow combine it all into one horrible bezoar ball in their heads, and then try to describe it all at once. For example, they tend to perceive negative results as failed studies. Being very clear about what aspects of the study we are actually trying to critique should help here.
  3. My favorite: instead of discussing papers, talk about pop science (post-press release articles that appear in the press). I think that's the most productive idea of all.

Wednesday, May 25, 2016

The Slow Professor (book review)

"The Slow Professor" by Maggie Berg and Barbara Seeber is a manifesto-like book about some important problems in modern academia. It was published a few weeks ago (I actually pre-ordered it), and if you have anything to do with academia, I do totally recommend that you read it. It's also rather short, which means that you can read it quickly (I hoped it would be a bit longer). Let me summarize what I liked and what I did not like about it in two lists below:

What I liked about "The Slow Professor":

  • It tackles one of the most important problems in modern academia: everybody are perpetually busy (applying for grants, publishing, working on committees), and nobody has time to think. People are ashamed to think (it does not feel like working); moreover, people are ashamed to read (in modern culture it does not feel like working either). And that's bad. The chapter about "what is bad" is the most relatable and passionate part of the book; the description is perfect, and to the point.
  • The book makes you think; it is definitely thought-provoking. It is also written a bit like a manifesto, so I felt energized after reading it. I wanted to change something! This feeling wears off in a few days, as it usually happens with manifestos, but it is definitely not a depressing book, which is really a feat for a book that in its core describes some important problems. Well done!
  • It is short, so you can read it quickly.
  • It actually offers some meaningful solutions, or at least points at some possible directions where these solutions may be.
  • It offers a nice slogan ("The slow professor" is a nice slogan!).

What I didn't like:

  • It is woefully short, and the solutions it offers are very limited. I guess it's the inevitable tradeoff, and I'd really rather read a short passionate book now, than a long thoughtful book in five years. It may be too late in five years! But it is really more of a manifesto than a guide; a pamphlet that names the issues and sets the goals. It is not a self-help book that would guide you through a series of exercises. You need to find the solution yourself. It invites you to be a part of a community though, which is really nice!
  • The book is relatively full of really bad neuroscience and psychology. It mentions serotonin, dopamine, oxytocin and neural plasticity - all incorrectly, and in ways that are totally irrelevant for the topic and the message of the book. As a neuroscientist, I don't usually read pop-science pieces about the brain, because it hurts, so I was not quite aware that the pop-science surrounding the mystery of the brain got that bad over the years. When you buy this book, please just ignore everything it says about how neuroscience "proves" which teaching and research methods work, and which don't. Just skip it without reading, it's all a bunch of nonsense. Also it cites a bunch of retracted and non-replicated (but famous) studies in psychology, so take all psychological claims with a spoonful of salt.
  • Finally, I find it annoying that when professional academics try to write a popular book they still default to academese, or at least half-academese. If feels that every sentence in this book is half-way between the world of the living and the world of the dead; even though sentences are readable and clear, they still have a strong smell of dusty, deathly, cryptic, mummified academese. It feels that the authors fought this tendency to the end, but still could not quite shake of the suffocating embrace of academic writing.
A great book though; I really recommend it. After reading the first half I felt that I need to buy a copy for every person in my department. After finishing it I felt a bit less passionate, but still told everybody about it and encouraged them to buy it. It's a very worthy read!

And also, on a personal note, I am so happy that teaching colleges, and Bard in particular, and maybe even Biology program in particular, are in a relatively good shape, as far as the problems described in the "Slow Professor" go. We actually do talk to each other, and it feels like we have a bit of time to think. We have teaching and grading in place of grant writing, so there is still a monster of "busyness" to fight, but it seems that we are actually fighting this battle already; driven by a slightly different motivation (trying to become better teachers), but still fighting. And there is definitely lots of space for improvement!

If there were a pin with a snail (from the cover), I'd totally buy it. The "slow professorial movement" is something I'd love to belong to!

Wednesday, May 18, 2016

On endorsing grad school to students

It's exam time, and seniors are about to leave the college. By now most of them have firm plans for next year: some have secured a job (usually as a research  assistant or technician in a lab), some will do a post-bac to finish their pre-med curriculum. We generally encourage students to take a gap year between college and applying to grad schools or med schools, as it seems to make lots of sense: they don't ruin their last semester in college by traveling to interviews, they get a chance to taste some "adult life" before plunging back to school. Try to work 9 to 5 before you commit to another giant educational project. Maybe you will like it, maybe you will not - either way it will give you a better point of reference.

What I find a bit hard about this whole fledgling stage is the grad school discussions, as they never feel comfortable. Students don't typically realize that the job situation is relatively abysmal, so it's probably my job to scare them. At the same time, it feels like many of them are strong enough, and actually have a good chance of succeeding in this game. Should I encourage them? Or should I scare them? What about students who seem to "default" to grad school, even though they are not that strong? Or what about those who suffer from impostor syndrome (or at least behaved really insecure for last 2 years, despite being brilliant)? How does gender and race play into it?

I have no idea how to even handle it. I guess that's another reason why I feel so strongly about recommending a gap year between college and grad school: it feels better, because it reduces responsibility. You know what, dear student of mine, don't make a decision now (while I'm kind of semi-responsible for it), but take a year off (forget about me), and then you'll be able to decide (and it won't be my fault anymore if you regret your decision later). Is it what I am doing? It feels like there's a hint of it actually.

But it doesn't seem a good solution, does it?

One good thing I can do is to maintain and foster connections with alums that followed different careers. For every weeping postdoc on the web there's at least one depressed and burned-out medical student, and a couple of office workers who claim to be "dead inside". If we, the faculty, show that life after college is multifaceted, in both good and bad, hopefully it will help our students to make good decisions.

Tuesday, May 17, 2016

Dedications

One thing that stoke me as strange and alien this spring are the dedication pages on the honors theses I read (in this college we call them "Senior Projects", but it's essentially the same genre). I don't really know whether it's a local thing, or a general american tradition, but each senior project I read so far comes with a really long, tearful, exalted dedication. Both parents are mentioned, sometimes siblings and other relatives, a couple of teachers (typically including all three board members - because you cannot really praise one without praising all three, right?), some friends, and always the significant answer (gosh, so risky!).

My memory is weak, and I don't really quite remember whether we had dedications in Russia. I think some people would put something like that in their works, but it was never more than one short row. And even then it was always kind of frowned upon. Especially before the grade was given. Or at least I remember it so.

But here it evolved into some kind of competition of praise. And these dedication pages really read like obituaries, or farewell addresses. So teary, so high-strung!

Sometimes it feels that the weaker the project - the longer the dedication page, but it is probably an illusion. I guess when the project is short, it just makes a long dedication page more noticeable. Still fun!

Thursday, May 12, 2016

Does citation network topology change over time?

A book I am reading makes in passing, and without a reference, a broad sweeping claim that "Internet is killing good research habits". It claims that in the olde times people would go to the library and actually read, while these days they just google, find a paper with most citations, and use it in their work.

It sounds plausible, but it feels like the opposite statement would also sound very plausible. In the old days you would read 3 papers, get used to citations from these papers, and got stuck with them for the rest of your career, while these days you can google-scholar or pubmed for any combination of keywords and find papers from far removed disciplines, institutions, working groups and subfields that you would have never found on your own.

In terms of citation network topology, the first claim implies that in the past the distribution of node orders was more uniform than now, while the second line of thought suggests that actually it might have been more extremely non-uniform (skewed) than now.

I tried to find out the truth by googling about "Citation network evolution" and other stuff like that, but could not find anything. Apparently the distribution was very skewed even in the past, with few papers receiving a status of "classics", and getting thousands of citations. This phenomenon is alive these days as well. But whether it became better or worse - I don't actually know. It would be a nice thing to look into, although I figure the process of generating the "citation inequality" is so slow (it takes about 10 years or more for a paper to become classics) that we probably just don't have access to old citation networks, as they are probably not fully digitized yet.

Or maybe I'm using wrong keywords.